This paper proposes and tests a methodology for the development of a simulation for individual tax returns in the United States, enabling students of taxation and interested parties to examine changes to the tax code, examine the effects of tax planning alternatives, and conduct repeated experimental testing on the tax return data. The simulation produced data for 147,000 tax returns, representing approximately 1% of the population of filed tax returns as noted by the IRS/SOI. We present the methodology on how we created the simulation and compare the tax returns of the simulation to the measures provided by the IRS. Our simulated return data very closely matched the number and combined dollar value of the IRS/SOI summary data at the adjusted gross income (AGI), state, and filing status levels.
US Individual Income Tax Return Simulated Data: A Methodology
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Alexander Pelaez, Deb Sledgianowski, Steven Petra, Jianbing Zhu, Nooshin Nejati; US Individual Income Tax Return Simulated Data: A Methodology. Journal of Emerging Technologies in Accounting 2021; doi: https://doi.org/10.2308/JETA-2020-055
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